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train.py
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train.py
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from email.policy import strict
from Utils import Config
import numpy as np
from sklearn.metrics import log_loss, roc_auc_score
from tqdm import tqdm
from torch.utils.tensorboard import SummaryWriter
from Utils import Logger
import random as r
from Model import *
class Trainer():
def __init__(self , filename) -> None:
config = Config(filename)
self.config = config
self.ID = filename.split('.')[0]
self.logger = Logger(config)
self.interval = config.interval
self.dataset = Dataset(config)
self.config.dataset = self.dataset
self.savedpath = config.savedpath
config.device = torch.device("cuda")
if os.environ["CUDA_VISIBLE_DEVICES"] != "":
self.model = eval(f"{config.model}")(config).cuda()
else:
self.model = eval(f"{config.model}")(config)
self.params, self.unparams = [], []
for name, _ in self.model.named_parameters():
if 'embedding' in name:
self.unparams.append(_)
self.params.append(_)
continue
if 'c_dnn' in name or 'lam' in name or 'tha' in name:#or 'embedding' in name:
self.unparams.append(_)
else:
self.params.append(_)
self.tune = True
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=config.learning_rate , weight_decay=config.weight_decay)
self.interval_cur = self.switch_interval = 300
self.loss_fn = nn.BCELoss()
self.best_auc = 0.
self.epoch = 0
self.early_stop_cnt = config.early_stop
self.config = config
self.draw_interval = len(self.dataset.train) // config.draw_loss_points
self.has_live = True
self.k_interval = 200
self.epoch = 0
print(config.dataset_name)
if hasattr(config , 'pretrain'):
self.savedpath = config.pretrain
self.resume()
def get_group_parameters(self, lr1, lr2):
params = list(self.model.named_parameters())
param_group = [
{'params':[p for n,p in params if 'c_dnn' in n],'lr': lr1, 'weight_decay': 0.},
{'params':[p for n,p in params if 'c_dnn' not in n],'lr': lr2, 'weight_decay': 1e-5}
]
return param_group
@property
def current_state(self):
return {
'optimizer': self.optimizer.state_dict(),
'model': self.model.state_dict() ,
'early_stop_cnt': self.early_stop_cnt ,
'best_auc':self.best_auc,
'epoch':self.step
}
def resume(self):
save_info = torch.load(self.savedpath)
related_params= {k:v for k,v in save_info['model'].items() if 'fine_tune_adaptiver' not in k}
self.model.load_state_dict(related_params, strict = False)
self.epoch = save_info['epoch'] + 1
self.best_auc = 0#save_info['best_auc']
print("model loaded !")
def run(self):
self.writer = SummaryWriter(self.config.logdir)
self.train_process()
self.evaluation_process()
self.writer.close()
def train_process(self):
for i in range(self.epoch , 1000):
self.step = i
self.interval_cur -= 1
if self.interval_cur < -self.switch_interval:
self.interval_cur = self.switch_interval
self.train_epoch(self.optimizer)
if i % self.interval == 0:
auc , logloss = self.test_epoch(self.dataset.val)
self.logger.record(self.step , auc ,logloss , 'val')
self.writer.add_scalars('VAL/AUC' , {self.ID : auc} , self.step)
self.writer.add_scalars('VAL/LOGLOSS' , {self.ID : logloss} , self.step)
if auc > self.best_auc:
self.has_live = True
print('find a better model !')
self.best_auc = auc
self.early_stop_cnt = self.config.early_stop
torch.save(self.current_state , self.savedpath + '_best')
else:
self.early_stop_cnt -= 1
if self.early_stop_cnt == 0:
return
def evaluation_process(self):
saved_info = torch.load(self.savedpath + '_best')
self.model.load_state_dict(saved_info['model'])
auc , logloss = self.test_epoch(self.dataset.test)
self.logger.record(self.step , auc ,logloss , 'test')
print(f"test , auc: {auc} , logloss: {logloss}")
self.writer.add_scalars('TEST/AUC' , {self.ID : auc} , 0 )
self.writer.add_scalars('TEST/LOGLOSS' , {self.ID : logloss} , 0)
with open('./metafile.out', 'a+') as f:
f.write(str(self.model.__class__) + ' ' + str(auc) + ' ' + str(logloss) + ' \n')
def train_epoch(self, optimizer):
cnt = 0
res = 0
self.model.train()
for fetch_data in tqdm(self.dataset.train) if self.config.verbose else self.dataset.train:
cnt += 1
optimizer.zero_grad()
prediction = self.model(fetch_data)
loss = self.loss_fn(prediction.squeeze(-1) , fetch_data['label'].squeeze(-1).cuda()) \
+ self.model.RegularLoss(weight = self.config.L2) \
loss.backward()
optimizer.step()
res += loss.cpu().item()
def test_epoch(self , datasource):
with torch.no_grad():
self.model.eval()
val , truth = [] , []
for fetch_data in tqdm(datasource) if self.config.verbose else datasource:
prediction = self.model(fetch_data)
val.append(prediction.cpu().numpy())
truth.append(fetch_data['label'].numpy())
y_hat = np.concatenate(val, axis=0).squeeze()
y = np.concatenate(truth, axis=0).squeeze()
auc = roc_auc_score(y, y_hat)
logloss = - np.sum(y*np.log(y_hat + 1e-6) + (1-y)*np.log(1-y_hat+1e-6)) /len(y)
return auc , logloss
if __name__ == '__main__':
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--config_files", help="path to load config", default="/home/data/tz/DAGFM_pytorch/RunTimeConf_Criteo/fibinet.yaml")
parser.add_argument("--gpu", help="path to load config", default=0)
args = parser.parse_args()
import os
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
import torch
from torch import nn
from Data.dataset import Dataset
from Model import *
setup_seed(2022)
trainer = Trainer(args.config_files)
trainer.run()